Scatter matplotlib
from matplotlib import pyplot as plt
import numpy as np
# 散点 横轴和纵轴都是特征
x = np.random.normal(0, 1, 100) # 均值为0 方差为1 正态分布
y = np.random.normal(0, 1, 100)
plt.scatter(x, y)
plt.show()
x = np.random.normal(0, 1, 100000)
y = np.random.normal(0, 1, 100000)
plt.scatter(x, y)
plt.show()
x = np.random.normal(0, 1, 100000)
y = np.random.normal(0, 1, 100000)
plt.scatter(x, y,alpha=0.1)
plt.show()
Iris load the data set
from sklearn import datasets
iris = datasets.load_iris()
iris.keys()
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])
#print(iris.DESCR)
iris.feature_names
['sepal length (cm)',
'sepal width (cm)',
'petal length (cm)',
'petal width (cm)']
iris.target
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
iris.target_names
array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
X = iris.data[:,:2]
X
array([[5.1, 3.5],
[4.9, 3. ],
[4.7, 3.2],
[4.6, 3.1],
[5. , 3.6],
[5.4, 3.9],
.
.
.
y = iris.target
y
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
# type(X)
X[:,1]
array([3.5, 3. , 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3. ,
3. , 4. , 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3. ,
3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.1, 3. ,
3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3. , 3.8, 3.2, 3.7, 3.3, 3.2, 3.2,
3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2. , 3. , 2.2, 2.9, 2.9,
3.1, 3. , 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3. , 2.8, 3. ,
2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3. , 3.4, 3.1, 2.3, 3. , 2.5, 2.6,
3. , 2.6, 2.3, 2.7, 3. , 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3. , 2.9,
3. , 3. , 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3. , 2.5, 2.8, 3.2, 3. ,
3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3. , 2.8, 3. ,
2.8, 3.8, 2.8, 2.8, 2.6, 3. , 3.4, 3.1, 3. , 3.1, 3.1, 3.1, 2.7,
3.2, 3.3, 3. , 2.5, 3. , 3.4, 3. ])
plt.scatter(X[:,0],X[:,1])
X[y==0].size
100
len(X[y==0][:,0])
50
X[y==0,0] #散点图的x轴 1shiy轴
array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. ])
plt.scatter(X[y==0,0],X[y==0,1],color='r')
plt.scatter(X[y==1,0],X[y==1,1],color='g')
plt.scatter(X[y==2,0],X[y==2,1],color='b')
output_23_1.png
plt.scatter(X[y==0,0],X[y==0,1],color='r',marker='+')
plt.scatter(X[y==1,0],X[y==1,1],color='g',marker='x')
plt.scatter(X[y==2,0],X[y==2,1],color='b',marker='o')
output_24_1.png
Petals dimension PETAL
X = iris.data[:,2:]
X
array([[1.4, 0.2],
[1.4, 0.2],
[1.3, 0.2],
X[y==0,0]
array([1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4,
1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1. , 1.7, 1.9, 1.6,
1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.5, 1.3,
1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4])
plt.scatter(X[y==0,0],X[y==0,1],color='r',marker='o')
plt.scatter(X[y==1,0],X[y==1,1],color='g',marker='x')
plt.scatter(X[y==2,0],X[y==2,1],color='b',marker='+')
output_28_1.png
Reproduced in: https: //www.jianshu.com/p/538f365bfb3e